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Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations G C A T T A C G G C A T genes Article Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations Laurent C. A. M. Tellier 1,2, Jennifer Eccles 2, Nathan R. Treff 2, Louis Lello 1,2,*, Simon Fishel 3,4 and Stephen Hsu 1,2 1 Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA; [email protected] (L.C.A.M.T.); [email protected] (S.H.) 2 Genomic Prediction, Inc., North Brunswick, NJ 08902, USA; [email protected] (J.E.); [email protected] (N.R.T.) 3 CARE Fertility Group, Nottingham NG8 6PZ, UK; professorfi[email protected] 4 School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L2 2QP, UK * Correspondence: [email protected] Abstract: Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more. PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of two individuals is at a lower disease risk, even when these two individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results. New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology. We review the advances described above and Citation: Tellier, L.C.; Eccles, J.; discuss related ethical considerations. Treff, N.; Lello, L.; Fishel, S.; Hsu, S. Embryo Screening for Polygenic Keywords: genomics; complex trait prediction; PRS; in vitro fertilization; genetic engineering Disease Risk: Recent Advances and Ethical Considerations. Genes 2021, 12, 1105. https://doi.org/10.3390/ genes12081105 1. Introduction Academic Editor: Sulev Koks Over a million babies are born each year via IVF [1,2]. It is not uncommon for IVF parents to have more than one viable embryo from which to choose, as typical IVF cycles Received: 8 April 2021 can produce four or five. The embryo that is transferred may become their child, while Accepted: 6 July 2021 the others might not be used at all. We refer to this selection problem as the “embryo Published: 21 July 2021 choice problem”. In the past, selections were made based on criteria such as morphology (i.e., rate of development, symmetry, general appearance) and chromosomal normality as Publisher’s Note: MDPI stays neutral determined by aneuploidy testing. with regard to jurisdictional claims in Recently, large datasets of human genomes together with health and disease histories published maps and institutional affil- have become available to researchers in computational genomics [3]. Statistical methods iations. from machine learning have allowed researchers to build risk predictors (e.g., for specific disease conditions or related quantitative traits, such as height or longevity) that use the genotype alone as input information. Combined with the precision genotyping of embryos, these advances provide significantly more information that can be used for Copyright: © 2021 by the authors. embryo selection to IVF parents. Licensee MDPI, Basel, Switzerland. In this brief article, we provide an overview of the advances in genotyping and This article is an open access article computational genomics that have been applied to embryo selection. We also discuss distributed under the terms and related ethical issues, although a full discussion of these would require a much longer conditions of the Creative Commons paper. Indeed, an in-depth review was recently published by Professor Julian Savulescu— Attribution (CC BY) license (https:// Director of the Oxford Uehiro Centre for Practical Ethics—and collaborators [4], which creativecommons.org/licenses/by/ we discuss below. Our purpose is to make bioethicists and philosophers more aware of 4.0/). Genes 2021, 12, 1105. https://doi.org/10.3390/genes12081105 https://www.mdpi.com/journal/genes Genes 2021, 12, 1105 2 of 8 recent scientific and technological breakthroughs (i.e., the current research frontier and state of the art), as well as to inform medical genomics and IVF researchers of some ethical perspectives. No attempt at an entirely comprehensive treatment of either scientific or ethical issues is contemplated, but we hope to further a well-informed discussion in this important area. 2. Polygenic Risk Scores (PRSs) Polygenic risk predictors for dozens of important disease conditions have been pub- lished and validated by numerous research groups around the world [5–12]. We can roughly characterize the performance of these polygenic risk predictors as follows: Indi- viduals with very high PRSs will typically have an incidence rate that is many times higher than the population average. For example, in [5], it was found that for atrial fibrillation, a 99th percentile PRS implies ∼10 times higher likelihood of case status. The rapid, nonlinear increase in absolute risk for the condition with the PRS percentile is shown in Figure1 below. For outliers at very high PRS percentiles (e.g., within the top 1%), risk can exceed that associated with well-known monogenic risk factors, such as BRCA1 and BRCA2 [7]. Absolute risk can even approach 1 (near certainty) for some individuals. 0.4 0.8 Breast cancer Hypothyroidism inferred population inferred population predicted predicted 0.3 lifetime prevalence 0.6 lifetime prevalence 0.2 0.4 Probability Probability 0.1 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Percentile PRS Percentile PRS Figure 1. Incidence of breast cancer and hypothyroidism as a function of the polygenic risk score (PRS) percentile. At a high PRS, the likelihood of incidence increases nonlinearly, and at a low PRS, the likelihood decreases nonlinearly. The red curve indicates the theoretical, modeling case, and control populations with normal distributions that were shifted in the mean PRS. The blue data points were calculated using individuals (not used in training) binned by the PRSs. Reproduced from [5]. There are now many validations of polygenic prediction in the scientific literature, which were conducted using groups of people born on different continents and in different decades with respect to the original populations used in training [10,13,14]. However, it is important to note that predictors work best when applied to ancestry groups that are similar to the original training population, and performance falls off with genetic distance [11,15]. It has also been shown that predictors can differentiate between siblings— for example, determining which one of them will experience a heart attack—despite similarity in childhood environments and genotype. The predictors work almost as well in pairwise sibling comparisons as in comparisons between randomly selected strangers [16]. Given one sibling with a normal-range PRS (less than the 84th percentile) and one sibling with a high PRS (e.g., the top 5 percentile, see [16] ), the predictors identify the affected sibling in about 70–90 percent of the cases across a variety of disease conditions, including breast cancer, heart attack, type 2 diabetes, and schizophrenia. For height, the predictor correctly identifies the taller sibling in roughly 80 percent of the cases when the (male) height difference is 2 inches or more [17]. There is already significant research on the application of PRSs in a clinical setting [5,8,12,18–26]. As a concrete example, women with high PRSs for breast cancer can be offered early screening—this is already the standard of care for those with BRCA risk Genes 2021, 12, 1105 3 of 8 variants [27,28]. However, BRCA mutations affect no more than a few women per thousand in the general population [29–31]. Importantly, the number of (BRCA-variant negative) women who are at high risk for breast cancer due to polygenic effects is an order of magni- tude larger than the population of BRCA-variant carriers [5,7,32–34]. Precision genetics are already used in the identification of candidates for early intervention and will become widespread in the near future (cf. Myriad’s riskScore test and other examples [33,34]). 3. Precision Embryo Genotyping Embryo biopsies (typically 3–7 cells) contain only a small amount of DNA, so it is a challenging problem to obtain accurate genotypes from them [35]. The problem is ameliorated by the widespread use of embryo freezing in IVF (in the past fresh embryo transfer required short turnaround times for genotyping results [36]), but amplification of small amounts of DNA still presents challenges for accurate genotyping. This problem has been solved by genomic prediction (GP) [35], thus allowing the application of PRSs in IVF. The GP process uses parental genotypes and the genotypes of other embryos (siblings) to perform error correction, achieving genotyping accuracy exceeding even that of clinical saliva genotyping on similar hardware platforms (99.6%), which is sufficient to accurately evaluate PRSs. This highly customized bioinformatics pipeline enables not only reliable polygenic disease prediction, but also other applications that rely on genotyping, such as fingerprinting, allelic ratio determination, polyploidy detection, relatedness QC checks, and contamination QC checks—resulting in a far superior performance in basic PGT (preimplantation genetic testing). A 99.6% genotyping accuracy means that the same sample genotyped twice will give the same clinical result twice, in contrast with noisier methods, such as traditional NGS (next-generation sequencing). Carmi et al.
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